class_docstr: 3-layer feed-forward neural networks with jumping connections
  Parameters
  -----------
  in_dim: int
      Input feature size.
  hid_dim: int
      Hidden feature size.
  
  Functions
  -----------
  forward(feat):
      feat: Tensor
          [N * D], input features
code: def forward(self, feat):
      block_out = self.block(feat)
      jump_out = self.jump_con(feat)
  
      out = block_out + jump_out
  
      return out
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import GINConv, NNConv, Set2Set
  from dgl.nn.pytorch.glob import SumPooling
  from torch.nn import BatchNorm1d, GRU, Linear, ModuleList, ReLU, Sequential
  from utils import global_global_loss_, local_global_loss_
label_desc: Use image node_id 2 for calling the FeedforwardNetwork obj's underlying member method code with example usage: obj.forward(feat) and returns: out